Description:
Business process re-engineering (part of the Business Process Management domain) is among the top three concerns of Information Technology (IT) leaders and is deemed to be one of many important IT leveraging opportunities. Two major challenges have been identified in relation to BPM and the use of IT. The first challenge is related to involving business process participants in process improvement initiatives using BPM systems. BPM technologies are considered to be primarily targeted for developers and not BPM users, and the need to engage process participants into process improvement initiatives is not addressed, contributing to the business-IT gap. The second challenge is related to potential de-skilling of knowledge workers when knowledge-intensive processes are automated and process knowledge resides in IT, rather than human process participants. The two identified challenges are not separate issues. Process participants need to be knowledgeable about the process in order to actively contribute to BPM initiatives, and the loss of process knowledge as a result of passive use of automated systems may further threaten their participation in process improvement. In response to the call for more research on the individual impacts of business process initiatives, the purpose of this dissertation study is to understand the relationship between IT configurations (particularly process support and process visualization), process characteristics and individual level process outcomes, such as task performance and process knowledge. In the development of the research model we rely on organizational knowledge creation literature and scaffolding in Vygotsky’s Zone of Proximal Development, business process modeling and workflow automation research, as well as research on the influence of IT on individual performance. The theoretical model is tested empirically in experimental settings using a series of two studies. In both studies participants were asked to complete tasks as part of a business process using different versions of a mock-up ...

Description:
Behavioral operations management (BOM) has gained popularity in the last two decades. The main theme in this new stream of research is to include the human behavior in Operations Management (OM) models to increase the effectiveness of such models. BOM is classified into 4 areas: cognitive psychology, social psychology, group dynamics and system dynamics (Bendoly et al. 2010). This dissertation will focus on the first class, namely cognitive psychology. Cognitive psychology is further classified into heuristics and biases. Tversky and Kahneman (1974) discussed 3 heuristics and 13 cognitive biases that usually face decision makers. This dissertation is going to study 6 cognitive biases under the representativeness heuristic. The model in this dissertation states that cognitive reflection of the individual (Frederick 2005) and training about cognitive biases in the form of warning (Kaufmann and Michel 2009) will help decisions’ makers make less biased decisions. The 6 cognitive biases investigated in this dissertation are insensitivity to prior probability, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity and misconception of regression. 6 scenarios in OM contexts have been used in this study. Each scenario corresponds to one cognitive bias. Experimental design has been used as the research tool. To see the impact of training, one group of the participants received the scenarios without training and the other group received them with training. The training consists of a brief description of the cognitive bias as well as an example of the cognitive bias. Cognitive reflection is operationalized using cognitive reflection test (CRT). The survey was distributed to students at University of North Texas (UNT). Logistic regression has been employed to analyze data. The research shows that participants show the cognitive biases proposed by Tversky and Kahneman. Moreover, CRT is significant factor to predict the cognitive bias in two ...

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In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for comparison studies. Since classification is considered an important human function and has been studied in the areas of cognitive science and information science, in this dissertation a research study was performed to compare LDA, LSA and humans as document classifiers. The research questions posed in this study are: R1: How accurate is LDA and LSA in classifying documents in a corpus of textual data over a known set of topics? R2: How accurate are humans in performing the same classification task? R3: How does LDA classification performance compare to LSA classification performance? To address these questions, a classification study involving human subjects was designed where humans were asked to generate and classify documents (customer comments) at two levels of abstraction for a quality assurance setting. Then two computer algorithms, LSA and LDA, were used to perform classification on these documents. The results indicate that humans outperformed all computer algorithms and had an accuracy rate of 94% at the higher level of abstraction and 76% at the lower level of abstraction. At the high level of abstraction, the accuracy rates were 84% for both LSA and LDA and at the lower level, the accuracy rate were 67% for LSA and 64% for LDA. The findings of this research have many strong implications for the ...

Description:
Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.

Description:
In 2012, the CIA World Fact Book showed that the service sector contributed about 76.6% and 51.4% of the 2010 gross national product of both the United States and Ghana, respectively. Research in the services area shows that a firm's success in today's competitive business environment is dependent upon its ability to deliver superior service quality. However, these studies have yet to address factors that influence customers to remain committed to a mass service in economically diverse countries. In addition, there is little research on established service quality measures pertaining to the mass service domain. This dissertation applies Rusbult's investment model of relationship commitment and examines its psychological impact on the commitment level of a customer towards a service in two economically diverse countries. In addition, service quality is conceptualized as a hierarchical construct in the mass service (banking) and specific dimensions are developed on which customers assess their quality evaluations. Using, PLS path modeling, a structural equation modeling approach to data analysis, service quality as a hierarchical third-order construct was found to have three primary dimensions and six sub-dimensions. The results also established that a country's national economy has a moderating effect on the relationship between service quality and investment size, and service satisfaction on investment size. This study is the first to conceptualize and use the hierarchical approach to service quality in mass services. Not only does this study build upon the investment model to provide a comprehensive decision model for service organizations to increase their return on investment but also, provides a congruence of work between service quality and the investment model in the management and decision sciences discipline.

Description:
Understanding the process of consumers during key purchasing decision points is the margin between success and failure for any business. The cultural differences between the factors that affect consumers in their decision-making process is the motivation of this research. The purpose of this research is to extend the current body of knowledge about decision-making factors by developing and testing a new theoretical model to measure how culture may affect the attitudes and behaviors of consumers in restaurants. This study has its theoretical foundation in the theory of service quality, theory of planned behavior, and rational choice theory. To understand how culture affects the decision-making process and perceived satisfaction, it is necessary to analyze the relationships among the decision factors and attitudes. The findings of this study contribute by building theory and having practical implications for restaurant owners and managers. This study employs a mixed methodology of qualitative and quantitative research. More specifically, the methodologies employed include the development of a framework and testing of that framework via collection of data using semi-structured interviews and a survey instrument. Considering this framework, we test culture as a moderating relationship by using respondents’ birth country, parents’ birth country and ethnic identity. The results of this study conclude, in the restaurant context, culture significantly moderates consumers’ perception of service quality, overall satisfaction, and behavior intention.of OA.

Description:
This study empirically examines the impact of IT capability on firms' performance and evaluates whether firms' IT capabilities play a role in improving employee capability, customer value, customer satisfaction, and ultimately business performance. The results were based on comparing the business performance of the IT leader companies with that of control companies of similar size and industry. The IT leader companies were selected from the Information Week 500 list published annually from 2001 to 2004. For a company to be selected as IT leaders, it needed to be listed at least twice during the period. Furthermore, it had to be listed in the American Customer Satisfaction Index (ACSI) so that its customer satisfaction level could be assessed. Standard & Poor's Compustat and the ACSI scores were used to test for changes in business performance. The study found that the IT leaders had a raw material cost measured by cost-of-goods-sold to sales ratio (COGS/S) than the control companies. However, it found no evidence that firms' IT capability affects employee capability, customer value, customer satisfaction, and profit. An important implication from this study is that IT becomes a commodity and an attempt to gain a competitive advantage by overinvesting in IT may be futile.

Description:
In information systems design there are two schools of thought about what factors are necessary to create a successful information system. The first, conventional view holds that system performance is a key, so that efficiency characteristics such as system usability and task completion time are primary concerns of system designers. The second, emerging view holds that the visual design is also the key, so that visual interface characteristics such as visual appeal, in addition to efficiency characteristics, are critical concerns of designers. This view contends that visual design enhances system use. Thus, this work examines the effects of visual design on computer systems. Visual design exerts its influence on systems through two mechanisms: it evokes affective responses from IT users, such as arousal and pleasure and it influences individuals’ cognitive assessments of systems. Given that both affective and cognitive reactions are significant antecedents of user behaviors in the IT realm, it is no surprise that visual design plays a critical role in information system success. Human-computer-interaction literature indicates that visual aesthetics positively influences such information success factors as usability, online trust, user satisfaction, flow experience, and so on. Although academic research has introduced visual design into the Information Systems (IS) field and validated its effects, visual design is still very limited in three contexts: product aesthetics in e-commerce, mobile applications and commercial emails. This dissertation presents three studies to help fill these theoretical gaps respectively.

Description:
The current generation of structural equation modeling (SEM) is loosely split in two divergent groups - covariance-based and variance-based structural equation modeling. The relative newness of variance-based SEM has limited the development of techniques that extend its applicability to non-metric data. This study focuses upon the extension of general linear model techniques within the variance-based platform of partial least squares structural equation modeling (PLS-SEM). This modeling procedure receives it name through the iterative PLS‑SEM algorithm's estimates of the coefficients for the partial ordinary least squares regression models in both the measurement model and the overall structural model. This research addresses the following research questions: (1) What are the appropriate measures for data segmentation within PLS‑SEM? (2) What are the appropriate steps for the analysis of rank-ordered path coefficients within PLS‑SEM? and (3) What is an appropriate model selection index for PLS‑SEM? The limited type of data to which PLS-SEM is applicable suggests an opportunity to extend the method for use with different data and as a result a broader number of applications. This study develops and tests several methodologies that are prevalent in the general linear model (GLM). The proposed data segmentation approaches posited and tested through post hoc analysis of structural model. Monte Carlo simulation allows demonstrating the improvement of the proposed model fit indices in comparison to the established indices found within the SEM literature. These posited PLS methods, that are logical transfers of GLM methods, are tested using examples. These tests enable demonstrating the methods and recommending reporting requirements.

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Description:
Ubiquitous computing results in access to vast amounts of data, which is changing the way humans interact with each other, with computers, and with their environments. Information is literally at our fingertips with touchscreen technology, but it is not valuable until it is understood. As a result, selecting which information to use in a decision process is a challenge in the current information environment (Lu & Yuan, 2011). The purpose of this dissertation was to investigate how individual decision makers, in different decision contexts, determine when to stop collecting information given the availability of virtually unlimited information. Decision makers must make an ultimate decision, but also must make a decision that he or she has enough information to make the final decision (Browne, Pitts, & Wetherbe, 2007). In determining how much information to collect, researchers found that people engage in ‘satisficing' in order to make decisions, particularly when there is more information than it is possible to manage (Simon, 1957). A more recent elucidation of information use relies on the idea of stopping rules, identifying five common stopping rules information seekers use: mental list, representational stability, difference threshold, magnitude threshold, and single criterion (Browne et al., 2007). Prior research indicates a lack of understanding in the areas of information use (Prabha, Connaway, Olszewski, & Jenkins, 2007) and information overload (Eppler & Mengis, 2004) in Information Systems literature. Moreover, research indicates a lack of clarity in what information should be used in different decision contexts (Kowalczyk & Buxmann, 2014). The increase in the availability of information further complicates and necessitates research in this area. This dissertation seeks to fill these gaps in the literature by determining how information use changes across decision contexts and the relationships between stopping rules. Two unique methodologies were used to test the hypotheses in the conceptual ...

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Description:
The social network sites (SNS) industry has recently shown an abnormal development pattern: An SNS could rapidly accumulate a large number of users, and then suffer a serious loss of users in a short time, which subsequently leads to the failure of the Web site in the highly competitive market. The user's social network site continuance is considered the most important factor for an SNS to keep its sustainable development. However, little knowledge of the user's SNS continuance raises the following research question: What factors could significantly influence the user's SNS continuance intention? To address this research question, I study the question from three lenses of research, including the I-view, the social interactivity view, and the trust based view. The I-view is an extension of the IS continuance model. From this research perspective, I tested the influence of the utilitarian factor (i.e., perceived usefulness) and the hedonic factor (i.e., perceived enjoyment) on the user's satisfaction in the I-view. In addition, I extend the umbrella construct, confirmation, into two sub-constructs, informativeness and self-actualization, and respectively study their influences on the utilitarian factor and the hedonic factor. I find that the user's perceived enjoyment has a significant positive effect on the user's satisfaction, thereby motivating the user to continue using the SNS. The perceived informativeness of an SNS and the user's self-actualization through information sharing with others on the Web site both have significant positive effects on the user's perceived usefulness and perceived enjoyment. From the social interactivity perspective, I suggest that a user's social gains could have a projection effect on the user's satisfaction in an SNS and his or her SNS continuance intention. Most previous studies emphasized on the influence of social connection outcomes (i.e., social capitals) on the user's behavioral intention, but ignored the fact that an individual would ...

Description:
This research seeks to derive and examine a multidimensional definition of information security awareness, investigate its antecedents, and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study is executed in two stages. First, the participants (employees of a municipality) are exposed to a series of phishing and spear-phishing messages to assess if there are any common characteristics shared by the phishing victims. The differences between the phished and the not phished group are assessed through multiple discriminant analysis. Second, the same individuals are asked to participate in a survey designed to examine their security awareness. The research model is tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. The study extends the theory by proposing and validating a universal definition of security awareness. It provides practitioners with an instrument to examine awareness in a plethora of settings and design customized security training activities.

Description:
Social software has become pervasive including technologies such as blogs, wikis, and social networking sites. Interactive Web 2.0 technology is distinguished from earlier Internet channels, with content provided not only from the website host, but also and most importantly, user-generated content. These social technologies are increasingly entering the enterprise, involving complex social and psychological aspects as well as an understanding of traditional technology acceptance factors. Organizations trying to reap potential benefits of enterprise social software (ESS) must successfully implement and maintain ESS tools. This research develops a framework for assessing knowledge sharing based on reciprocal determinism theory and augmented with technology acceptance, sociological, and psychological factors. Semi-structured interviews with IT professionals, followed by a written survey of employees using ESS are used to collect data. The hermeneutic circle methodology is used to analyze the interview transcripts and structural equation modeling is used to analyze the survey data. Results show technological advantage has no significant effect on the intention to share knowledge, but community cohesiveness and individual willingness significantly affect knowledge sharing intention and behavior. The study offers a synthesized model of variables affecting knowledge sharing as well as a better understanding of best practices for organizations to consider when implementing and maintaining ESS tools for employee knowledge sharing and collaboration.

Description:
Converting individual knowledge into organizational knowledge can be difficult because individuals refuse to share knowledge for a number of different reasons. Creating an atmosphere of fairness plays an important role in the creation of a knowledge-sharing climate. This dissertation proposes that perceptions of organizational justice are crucial building blocks of that environment, leading to knowledge sharing. Data was collected using a field survey of IT managers representing a broad spectrum of the population in terms of organizational size and industry classification. The survey instrument was developed based on the adaptation of previously validated scales in addition to new items where no existing measures were found. Hypotheses regarding the influence of distributional, procedural, and interactional justice on knowledge sharing processes were tested using structural equation modeling techniques. Based on the theory of reasoned action, which states that attitudes and subjective norms are the major determinants of a person's intention, the hypotheses examining the relationship between attitude toward knowledge sharing, subjective norm and the intention to share knowledge were supported. However, results did not support the hypothesis exploring the relationship between the organizational climate and the intention to share knowledge. The results show that all three types of justice constructs are statistically significant antecedents of organizational climate and interactional justice is an antecedent of an attitude toward knowledge sharing. The study attempts to merge streams of research from sociology and organizational behavior by investigating organizational justice and knowledge management. It contributes to theory by the development of the survey instrument, comprised of seven constructs that were developed by incorporating multiple theories to address various aspects of knowledge sharing and provide application to practice and research. It is relevant to IT managers who need to know how to design information systems that are most effective in distributing knowledge throughout organizations.

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Description:
Since the concept of business intelligence (BI) was introduced in the late 1980s, many organizations have implemented BI to improve performance but not all BI initiatives have been successful. Practitioners and academicians have discussed the reasons for success and failure, yet, a consistent picture about how to achieve BI success has not yet emerged. The purpose of this dissertation is to help fill the gap in research and provide a better understanding of BI success by examining the impact of BI capabilities on BI success, in the presence of different decision environments. The decision environment is a composition of the decision types and the way the required information is processed to aid in decision making. BI capabilities are defined as critical functionalities that help an organization improve its performance, and they are examined in terms of organizational and technological capabilities. An online survey is used to obtain the data and partial least squares path modeling (PLS) is used for analysis. The results of this dissertation suggest that all technological capabilities as well as one of the organizational capabilities, flexibility, significantly impact BI success. Results also indicate that the moderating effect of decision environment is significant for quantitative data quality. These findings provide richer insight in the role of the decision environment in BI success and a framework with which future research on the relationship between BI capabilities and BI success can be conducted. Findings may also contribute to practice by presenting information for managers and users of BI to consider about their decision environment in assessing BI success.

Description:
This study, utilizing Preston and Karahanna’s framework for shared vision development and Agency Theory, explores the impact of vision development factors and factors associated with incentive plans on shared vision and alignment. Results of the study confirm the strong relationship between shared vision and alignment, and indicate that having an effective management team is important with respect to developing and maintaining shared vision and alignment within the organization. Several vision development factors such as using the language of the business, participation on the top management team (TMT), and having knowledge of the business impact shared vision through their influence on teamwork. Also, results of this study suggest that participation on the TMT by the CIO/IT leader is more important than the individual’s position in the organizational hierarchy. In addition, attributes associated with incentive plans such as achievable and clear measures, measures linked to organizational goals, measures that align the interests of the individual with those of the organization, regular plan reviews, and using a balanced scorecard approach with respect to incentive plan design positively impact teamwork and shared vision. For practitioners, this highlights the importance of incentive plans as powerful tools that can be used to reinforce shared vision, effective teamwork, and alignment within the organization. Also, the CIO/IT leader needs to be knowledgeable of the business and must fill the role of both a technologist as well as an enterprise leader. This person must be an evangelist communicating the value and benefits of IT to the rest of the organization.

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With advances in computer technology, organizations are able to store large amounts of data in data warehouses. There are two fundamental issues researchers must address: the dimensionality of data and the interpretation of multiple statistical tests. The first issue addressed by this research is the determination of the number of components to retain in principal components analysis. This research establishes regression, asymptotic theory, and neural network approaches for estimating mean and 95th percentile eigenvalues for implementing Horn's parallel analysis procedure for retaining components. Certain methods perform better for specific combinations of sample size and numbers of variables. The adjusted normal order statistic estimator (ANOSE), an asymptotic procedure, performs the best overall. Future research is warranted on combining methods to increase accuracy. The second issue involves interpreting multiple statistical tests. This study uses simulation to show that Parker and Rothenberg's technique using a density function with a mixture of betas to model p-values is viable for p-values from central and non-central t distributions. The simulation study shows that final estimates obtained in the proposed mixture approach reliably estimate the true proportion of the distributions associated with the null and nonnull hypotheses. Modeling the density of p-values allows for better control of the true experimentwise error rate and is used to provide insight into grouping hypothesis tests for clustering purposes. Future research will expand the simulation to include p-values generated from additional distributions. The techniques presented are applied to data from Lake Texoma where the size of the database and the number of hypotheses of interest call for nontraditional data mining techniques. The issue is to determine if information technology can be used to monitor the chlorophyll levels in the lake as chloride is removed upstream. A relationship established between chlorophyll and the energy reflectance, which can be measured by satellites, enables ...

Description:
The increasing demand for mobile apps is out the current capability of mobile app developers. In addition, the growing trend in smartphone ownership and the time people spend on mobile apps has raised several opportunities and risks for users and developers. The average time everyday a user spend on smartphones to use mobile apps is more than two hours. The worldwide mobile app revenue increase is estimated to grow 33%, $19 billion. Three quarter of the time used on mobile apps is solely for using game and social networking apps. To provide more customized services and function to users, mobile apps need to access to personal information. However, 80% of mobile apps put people's information privacy at risk. There is a major gap in the literature about the privacy concerns of mobile device users in the context of mobile apps. This dissertation addresses one fundamental research question: how does individuals' privacy change in the context of mobile apps? More precisely, the focus of this dissertation is on information privacy role in individuals' and mobile app developers' protective behaviors. We investigate the information sensitivity level influence on mobile app developers' emphasis on privacy across mobile app categories. The results show information sensitivity level has a significant impact on developers' emphasis on secondary usage of information. Moreover, we analyze the privacy trade-off dynamism in using a new social networking app and how it could result in emotional attachment. Results show initial use and initial disclosure influence the privacy trade-off from pre-use to initial-use period. Finally, the effect of privacy concern and engagement on emotional attachment is demonstrated. This dissertation addresses one fundamental research question: how does individuals' privacy change in the context of mobile apps? More precisely, the focus of this dissertation is on information privacy role in individuals' and mobile app ...

Description:
Despite much interest in service quality and various other service quality measures, scholars appear to have overlooked the overall concept of quality. More specifically, previous research has yet to integrate the effect of the customer network and customer knowledge into the measurement of quality. In this work, it is posited that the evaluation of quality is based on both the delivered value from the provider as well as the value developed from the relationships among customers and between customers and providers. This research examines quality as a broad and complex issue, and uses the “Big Quality” concept within the context of routine healthcare service. The last few decades have witnessed interest and activities surrounding the subject of quality and value co-creation. These are core features of Service-Dominant (S-D) logic theory. In this theory, the customer is a collaborative partner who co-creates value with the firm. Customers create value through the strength of their relations and network, and they take a central role in value actualization as value co-creator. I propose to examine the relationship between quality and the constructs of value co-creation. As well, due to the pivotal role of the decision-making process in customer satisfaction, I will also operationalize the value co-creation construct. Building upon the “Big Quality” concept, this study suggests a new approach by extending the quality concept to include the value-creation concept in Service Dominant Logic. This study identifies the associated constructs and determinants of Big Quality in routine healthcare management service, and examines the relationship among the associated quality constructs, customer satisfaction, and customer commitment. This study employed an online survey methodology to collect data. In data analysis, I used the variance-based structural equation modeling (PLS-SEM) approach to confirm the factor structure, proposed model, and test the research hypotheses. The results show that the customer’s ...

Description:
Application of multisource feedback (MSF) increased dramatically and became widespread globally in the past two decades, but there was little conceptual work regarding self-other agreement and few empirical studies investigated self-other agreement in other cultural settings. This study developed a new conceptual framework of self-other agreement and used three samples to illustrate how national culture affected self-other agreement. These three samples included 428 participants from China, 818 participants from the US, and 871 participants from globally dispersed teams (GDTs). An EQS procedure and a polynomial regression procedure were used to examine whether the covariance matrices were equal across samples and whether the relationships between self-other agreement and performance would be different across cultures, respectively. The results indicated MSF could be applied to China and GDTs, but the pattern of relationships between self-other agreement and performance was different across samples, suggesting that the results found in the U.S. sample were the exception rather than rule. Demographics also affected self-other agreement disparately across perspectives and cultures, indicating self-concept was susceptible to cultural influences. The proposed framework only received partial support but showed great promise to guide future studies. This study contributed to the literature by: (a) developing a new framework of self-other agreement that could be used to study various contextual factors; (b) examining the relationship between self-other agreement and performance in three vastly different samples; (c) providing some important insights about consensus between raters and self-other agreement; (d) offering some practical guidelines regarding how to apply MSF to other cultures more effectively.

Description:
Understanding information technology and its related products and services is increasingly important because the everyday use of technology continues to expand and broaden. Despite this need for greater understanding, the extant theories that explore the dominant factors that drive intention to use a new technology are limited. The Technology Acceptancy Model (TAM) is the most popular model in explaining traditional technology adoption. The limitations of the TAM in grasping the overall evaluation of technology or service are one of motivations for developing new models in this dissertation. The two antecedents of the TAM- perceived usefulness and perceived ease of use- only capture partial utility of a service (or product). In addition, some researchers argued that key factors used in an initial acceptance model such as perceived usefulness and perceived ease of use are not strong predictors of future continuance intention of the service because they do not consider future switching intention in the later stage. Hence, one goal of this dissertation is to develop and test new models to predict factors that drive intention and continuance intention decisions of new technology related products or services. This research involves three studies that examine different aspects of adoption and continuance intention decisions of new technology-related products or services. Essay 1 posits and empirically tests a new model that examines service vendor quality, service outcome quality, and trust as drivers of cloud-based services by adopting the frameworks from marketing, behavior intention and information technology research. The model is referred to as the quality trust model (QTM). The quality of cloud-based services involves the quality of vendors and the quality of service outcomes; its effectiveness is mirrored by trust of the services. Data from an online survey of 355 respondents were used to test the research model. The results show that vendor quality and ...

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Online users struggle with different concerns whenever they use information systems. According to Miyazaki and Fernandez (2001), there are three important categories of concerns for online users: privacy concern, third party fraudulent behavior concern ("system security"), and online website fraudulent behavior concern ("security"). Kim, Sivasailam, and Rao (2004) proposed a similar categorization for web assurance dimensions. They argue that online websites are supposed to address users' privacy, security, and business integrity concerns to decrease user concerns. Although several researchers tried to answer how different factors affect these concerns and how these concerns affect users' behavior, there are so many ambiguities and contradictions in this area. This Essay I in this work develops a comprehensive map of the role of online privacy concern to identify related factors and categorize them through an in-depth literature review and conducting meta-analysis on online privacy concern. Although users have concerns about their privacy and security, there is still growth in the number of internet users and electronic commerce market share. One possible reason is that websites are applying assurance mechanisms to ensure the privacy of their users. Therefore, it could be an interesting research topic to investigate how privacy assurance mechanisms affect users concern and, consequently, their behavior in different concerns such as e-commerce and social networking sites. Different types of web assurance mechanisms are used by websites. The most prevalent among these assurance mechanisms include web assurance seals and assurance statements and privacy customization features. Essay II and III aims to address how these mechanisms influence e-commerce and social networking sites users' behavior. Essay II applies the procedural fairness theory by Lind and Tyler (1988) to explain how and why the web assurance mechanisms affect consumers' perceived risks. Essay III addresses the issue of self-disclosure on social networking sites. Applying protection motivation theory, this ...

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Description:
Over the past few years, there has been a skyrocketing growth in the use of mobile devices. Mobile devices are ushering in a new era of multi-platform media and a new paradigm of “being-always-connected”. The proliferation of mobile devices, the dramatic growth of cloud computing services, the availability of high-speed mobile internet, and the increase in the functionalities and network connectivity of mobile devices, have led to creation of a phenomenon called BYOD (Bring Your Own Device), which allows employees to connect their personal devices to corporate networks. BYOD is identified as one of the top ten technology trends in 2014 that can multiply the size of mobile workforce in organizations. However, it can also serve as a vehicle that transfers cyber security threats associated with personal mobile devices to the organizations. As BYOD opens the floodgates of various device types and platforms into organizations, identifying different sources of cyber security threats becomes indispensable. So far, there are no studies that investigated how user, device and usage characteristics affect individuals’ protective and risky IT behaviors. The goal of this dissertation is to expand the current literature in IS security by accounting for the roles of user, device, and usage characteristics in protective and risky IT behaviors of individuals. In this study, we extend the protection motivation theory by conceptualizing and measuring the risky IT behaviors of individuals and investigating how user, device, and usage characteristics along with the traditional protection motivation factors, influence individuals’ protective and risky IT behaviors. We collected data using an online survey. The results of our study show that individuals tend to engage in different levels of protective and risky IT behaviors on different types of devices. We also found that certain individual characteristics as well as the variety of applications that individuals use on their ...

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Description:
Today's environment is filled with the proliferation of cyber-attacks that result in losses for organizations and individuals. Hackers often use compromised websites to distribute malware, making it difficult for individuals to detect. The impact of clicking through a link on the Internet that is malware infected can result in consequences such as private information theft and identity theft. Hackers are also known to perpetrate cyber-attacks that result in organizational security breaches that adversely affect organizations' finances, reputation, and market value. Risk management approaches for minimizing and recovering from cyber-attack losses and preventing further cyber-attacks are gaining more importance. Many studies exist that have increased our understanding of how individuals and organizations are motivated to reduce or avoid the risks of security breaches and cyber-attacks using safeguard mechanisms. The safeguards are sometimes technical in nature, such as intrusion detection software and anti-virus software. Other times, the safeguards are procedural in nature such as security policy adherence and security awareness and training. Many of these safeguards fall under the risk mitigation and risk avoidance aspects of risk management, and do not address other aspects of risk management, such as risk transfer. Researchers have argued that technological approaches to security risks are rarely sufficient for providing an overall protection of information system assets. Moreover, others argue that an overall protection must include a risk transfer strategy. Hence, there is a need to understand the risk transfer approach for managing information security risks. Further, in order to effectively address the information security puzzle, there also needs to be an understanding of the nature of the perpetrators of the problem – the hackers. Though hacker incidents proliferate the news, there are few theory based hacker studies. Even though the very nature of their actions presents a difficulty in their accessibility to research, a glimpse of ...

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Filter: Discipline

This dialog allows you to filter your current search.
Each of the Discipline listed note their name and the number of records that will be limited down to if you choose that option.
The list can be sorted by name or the count.